The present invention relates to devices and methods for identifying unknown chemical compounds under field conditions. In particular, the present invention relates to devices and methods of using remote passive infra-red spectroscopy, in addition to spectral data, to identify the chemical compounds.
There are often instances where emergency response personnel (“first responders”) or military personnel are called to an accident scene or other incident where some type of chemical has been release and are confronted with gaseous cloud or plume, the chemical contents of which are unknown. Such situations can occur when railroad tank cars or highway transport tank trailers carrying chemicals are involved in an accident or when an accidental chemical release occurs at a chemical manufacturing facility. Obviously, knowing the contents of the plume is critical to decisions concerning how to contain the emergency, what protective gear is required, and whether there is a need to evacuate the local population. Methods of using remote passive infra-red (IR) spectrometers to attempt to identify the chemical compounds in a gaseous plume are known in the art. As used herein, the term “passive” implies that the spectrometer employs no specialized source of infrared photons (as opposed to “active” systems, which employ photons from optically optimized, high-temperature sources). The term “remote” indicates that the sample gases of interest are external to the spectrometer such as a plume at an accident site. Closed path spectroscopy may include extractive systems, which interrogate samples in an absorption cell, or use a well known photon source and a well known path length.
The general concept of IR spectroscopy in this situation is illustrated in FIG. 1. A passive spectrometer 101 will be positioned such that the gaseous plume 103 is between the spectrometer and some background object 105, which will provide a source of IR energy. In the case where the background 105 is significantly warmer than the plume 103, IR energy emitting from background 105 will pass through plume 103 and be recorded by spectrometer 101. Because different chemical compounds tend to absorb different wavelengths of IR energy, a measurement of the relative intensities of different frequencies of IR energy received at the spectrometer will provide information which may be used to identify the compounds in plume 103. In some cases, where the plume is warmer than its surroundings, the compounds within the plume may themselves emit (rather than absorb) at these IR frequencies. This absorbance is linearly related to the gaseous concentrations through the Beer-Lambert relation, or “Beer's Law.” When infrared radiation passes linearly through a gas sample for a distance L, its initial intensity (I0) is decreased through gaseous absorption to the level (I) measured at the spectrometer. Beer's Law states that the absorbance at each infrared frequency is defined by the relationship:                     A        ≡                  -                                    log              10                        ⁡                          (                              I                                  I                  0                                            )                                                          Eq        .                                   ⁢                  (          0          )                    The detailed function of a conventional spectrometer is explained in references such as U.S. Pat. No. 5,982,486, which is incorporated by reference, and need not be detailed herein. It is sufficient to understand from FIG. 2 that the spectrometer will initially create an “interferogram” (step 107) representing the space-domain response of its detector to the infrared radiation incident on the detector. The application of a fast-Fourier transform to the interferogram will form a sample single-beam (step 109) representing the total power incident on the infrared detector as a function of infrared frequency, the latter usually being expressed in units of “wavenumbers”, or reciprocal centimeters (cm−1). An illustration of a sample single-beam spectrum is seen in the lower trace of FIG. 3.
Because passive IR spectroscopy employs a background source of IR energy, the signals of real interest, namely the relatively narrow absorption and/or emission bands of the gases located between the spectrometer and the backdrop, are superimposed upon the smooth, broad emission spectrum of the background IR source. It is therefore necessary to develop some type of background spectrum. While the present invention's method of forming the background spectrum is described below, the middle trace of FIG. 3 illustrates the background single-beam spectrum graphically which will aid in understanding conceptually the invention's background.
Once the sample single-beam spectrum (step 111) and background single-beam spectrum (step 112) are determined, the sample absorbance spectrum may be calculated. In the notation adopted below, the sample absorbance spectrum (step 117) is defined in terms of the single beam sample spectrum SBiS and the single beam background spectrum SBiB as:                     A        ≡                  -                                    log              10                        ⁡                          (                                                S                  ⁢                                                                           ⁢                                      B                    i                    S                                                                    S                  ⁢                                                                           ⁢                                      B                    i                    B                                                              )                                                          Eq        .                                   ⁢                  (          1          )                    The upper trace of FIG. 3 illustrates the sample absorbance spectrum.
Once the absorption spectrum for the sample (“sample absorbance spectrum”) is created, it can be compared to the know absorption spectra for various chemical compounds (“reference spectra”) which may be represented in the sample absorbance spectrum. The reference spectrum of a compound may be considered the graphical representation of the degree of absorbance a chemical compound exhibits at those frequencies at which the compound absorbs IR radiation. Figuratively speaking, the reference spectrum is the IR “figure print” of a chemical compound. There are many methods for determining how closely a part of the sample absorbance spectrum matches a reference spectrum and thus with how much confidence it can be concluded that the reference chemical (i.e. the compound represented by the reference spectrum) exists in the sample gas. One method of comparing a sample absorbance spectrum to one or more reference spectra, is the utilization of a classical least squares analysis.
The use of Classical Least Squares (CLS) analyses is known in the art and has been used in spectral analysis as evidenced by publications such as D. M. Haaland and R. G. Easterling, “Improved Sensitivity of Infrared Spectroscopy by the Application of Least Squares Methods,” Appl. Spectrosc. 34(5):539-548 (1980); D. M. Haaland and R. G. Easterling, “Application of New Least-Squares Methods for the Quantitative Infrared Analysis of Multicomponent Samples,” Appl. Spectrosc. 36(6):665-673 (1982); D. M. Haaland, R. G. Easterling and D. A. Vopicka, “Multivariate Least-Squares Methods Applied to the Quantitative Spectral Analysis of Multicomponent Samples,” Appl. Spectrosc. 39(1):73-84 (1985); W. C. Hamilton, Statistics in Physical Science, Ronald Press Co., New York, 1964, Chapter 4 and references therein; and U.S. Pat. No. 5,982,486, all of which are incorporated by reference herein. While a full mathematical description of CLS is disclosed in the above references, a brief description, particularly in terms of matrix manipulation, will provide a useful background.
CLS analyses are generally useful in estimating the solutions of an over-determined system of linear equations; such a system of equations can always be represented by a matrix equation of the formA=DX+E  Eq. (2) where:
A=a set of N measured data, represented by a row vectorX=a set of M parameters to be estimated, representedby a column vectorD=The “design matrix,” with N rows and M columns,describing the linear mathematical relationship betweenthe measured data A and the parameters XE=The error in the linear model for each of the measured data,represented by a row vector of length N.As it relates to the comparison of a sample spectrum to one or more reference spectra, the matrix:A=[A1S, A2S, . . . ANS], will represent the sample spectrum with each member AiS of A representing an intensity value at the wavenumber νi over the frequency range [ν1, ν2, . . . νN]. In the design matrix:                     D        =                  (                                                                      A                  11                  R                                                                              A                  12                  R                                                            ·                                                              A                                      1                    ⁢                    M                                    R                                                            1                                                              v                  1                                                                                                      A                  21                  R                                                                              A                  22                  R                                                            ·                                            ·                                            1                                                              v                  2                                                                                    ·                                            ·                                            ·                                            ·                                            ·                                            ·                                                                                      A                  N1                  R                                                                              A                  N2                  R                                                            ·                                                              A                                      N                    ⁢                                                                                   ⁢                    M                                    R                                                            1                                                              v                  N                                                              )                                    Eq        .                                   ⁢                  (          3          )                    each column will represent a reference spectrum, with each member of a column AilR (using the first column as an example) representing an intensity value of the reference spectrum over the same frequency range as the absorption spectrum.
In the case N>M (that is, when the number of measured data exceeds the number of parameters to be estimated), the system of equations described in Equation 2 is referred to as “over-determined.” In this case, which pertains to all the CLS applications described here, there is no unique solution to Equation 2. However, it possible in this case to form estimates of the parameters {overscore (X)} and to characterize the accuracy of those estimates. Such estimates and characterizations may be based on any chosen set of mathematical criteria and constraints. A widely used criterion is to form estimates of the parameters {overscore (X)} that minimize the “weighted sum of squared residuals” for the model of Equation 2; this sum may (or may not) be defined in such a way as to account for variations in the quality of the measured data Ai. Such estimates are broadly referred to as the results of “least squares” techniques, and only such estimates are described in this work. The term “classical least squares” refers to least squares techniques based solely on the linear model described in Equation 2; other least squares techniques, e.g. those referred to as “partial least squares” analyses, often employ additional processing of the data and result in more complex approaches to estimations of the desired parameters {overscore (X)}.
Formally, “classical least squares” estimates are based on the assumptions that the error vector E possesses a joint distribution with zero means and a variance-covariance matrix that Mf of rank N. Many CLS estimates also include the further assumptions that the matrix Mf is known to within a (non-negative) scaling factor σ2, i.e. that                               M          f                =                                            σ              2                        ⁡                          (                                                                                          N                      11                                                                                                  N                      21                                                                            ·                                                                              N                      N1                                                                                                                                  N                      12                                                                                                  N                      22                                                                            ·                                                        ·                                                                                        ·                                                        ·                                                        ·                                                        ·                                                                                                              N                                              1                        ⁢                        N                                                                                                  ·                                                        ·                                                                              N                                              N                        ⁢                                                                                                   ⁢                        N                                                                                                        )                                =                                    σ              2                        ⁢            N                                              Eq        .                                   ⁢                  (          4          )                    Practically, Equation 2 embodies the assumption the relative quality of the measured data Ai is known. The relative quality of the data may be quantified by assigning a non-negative “weight” Pii to each Ai where the diagonal weight matrix P is (in general) the matrix inverse of N, i.e.P=N−1  Eq. (5) and therefore, according to Equation 5, that                               P                      i            ⁢                                                   ⁢            i                          =                  1                      N                          i              ⁢                                                           ⁢              i                                                          Eq        .                                   ⁢                  (          6          )                    
The type of CLS analyses commonly used in the prior art will be designated “unweighted CLS” in order to distinguish it from a “weighted CLS” analysis described below in the Detailed Description of the invention. Both types of CLS analysis employ the assumptions noted above in conjunction with Equations 4, 5, and 6.
Unweighted CLS analysis is one in which all measured data are assumed to be of equal quality; i.e. in the unweighted case, both the matrices P and N are equal to the identity matrix I. For any estimated set of parameters {overscore (X)}, the residual V is defined as:V≡A−D{overscore (X)}  Eq. (7) and the “weighted sum of squared residuals” is defined as:V2≡VtPV=(A−D{overscore (X)})tP(A−D{overscore (X)})  Eq. (8) where the superscript “t” denotes the matrix transpose.
The following estimate of the parameters {overscore (X)} exists, is unique, and leads to a minimum in V2 (the “weighted sum of squared residuals”):{overscore (X)}=(DtPD)−1DtPA  Eq. (9) Equation 9 describes the basic CLS parameter estimates which are useful and accurate in a number of applications. However, it is important to note that all CLS analyses also provide useful statistical measures of the uncertainties in the parameter estimates.In particular, CLS analyses provide a “marginal standard deviation” (MSD) for each parameter estimate. Where the CLS estimate of the variance-covariance matrix is{overscore (M)}=V2(DtPD)−1  Eq. (10) the marginal standard deviation (MSD) associated with each parameter estimate {overscore (X)}j is{overscore (Δ)}j=√{square root over({overscore (M)})}jj  Eq. (11) The MSD is sometimes referred to as the “1σ uncertainty” in the associated parameter. The relative magnitude of {overscore (X)}j and {overscore (Δ)}j is often used as an indicator of the quality of the CLS analysis estimate of the parameter {overscore (X)}j.
Weighted CLS analysis, which forms part of the invention discussed below, is one in which at least one Pii differs from the other values in the matrix P, that is, when at least one datum Ai is assumed to be of better or worse quality than the other Ai.
In addition to the spectrometry aspects of the present invention, this invention also relates both to novel methods for processing spectral data and novel methods of identifying chemical compounds based upon the location of a chemical release and conditions (e.g. colors, smells, and the like) observed at the site of the chemical release. This non-spectral method of identifying potential chemical compounds may be used in combination with or independently of spectral methods. When used in combination with spectral methods, the non-spectral methods will function to identify or aid in identifying an initial list of chemical compounds whose reference spectra will be chosen for comparison with the undetermined sample spectrum.